Why do most deep learning papers not include an implementation?
The paper's authors needed to implement their models anyway in order to conduct their experimentations, so why not publish the implementation?

Some papers and authors actually provide a link to their own implementation, but most of the papers (that I have read) don't provide it, although some third-party implementations may already be available on Github (or other code-hosting sites) when you are reading the paper. There may be different reasons why the author(s) of a paper don't provide a reference implementation

They use some closed-source software or maybe it makes use of other resources that cannot be shared
Their implementation is a mess and, so, from a pedagogical point of view, it's quite useless
This may encourage other people to try to reproduce their results with different implementations, so it may indirectly encourage people to do research on the same topic (but maybe not providing a reference implementation could actually have the opposite effect!)


Plus, if the implementation is not added and there's no reproducibility, what prevents authors from forging results?

Actually, that's a good question that only researchers that have already published something or some reviewers can say something about it. I would also like to know their take on this!
Nevertheless, from some reviews of papers I have read (e.g. on OpenReview), in most cases, the reviewers are interested in the consistency of the results, the novelty of the work, the clarity and structure of the paper, etc. I think that, in most cases, they probably trust the provided results, also because, often, for reproducibility, researchers are expected to describe their models and parameters in detail, provide plots, etc., but I don't exclude that there are cases of people that try to fool the reviewers. For example, watch this video where Phil Tabor comments on ridiculous attempts to fool people and plagiarism by Siraj Raval.